Manage and control pests infestation using machine learning in conjunction with automated devices

ABSTRACT

Embodiments of the present invention provides a systems and methods for pest control. The system detects one or more pests based on receiving sensor data from one or more sensors associated with a predefined location. The system analyzes the sensor data with cognitive machine learning based on the detected pests. The system generates a treatment recommendation report based on the analysis and outputs the treatment recommendation report.

BACKGROUND OF THE INVENTION

The present invention relates generally to the field of pest control andmore particularly to automated pest control.

Pest and pathogens cost global agriculture $540 billion dollars a yearand termites can cause an additional $5 billion a year to homeowners. Byidentifying an infestation early, farmers or homeowners can takeproactive actions (e.g., organic, chemical or physical) to prevent thepest problem from spreading and ruining homes and/or crops.

One of the phase regarding the current method of pest detectiontypically involves visual identification and frequent monitoring. Theother phases involves actual treatment of pests. Regular observation isalso critically important. Observation can broken into inspection andidentification steps. Visual inspection, insect traps, and other methodsare used to monitor pest levels. In addition, record-keeping is alsovitality essential. Furthermore, knowledge target pest behavior,reproductive cycles and ideal temperature. Sometimes, visualidentification of the affected area can even be too late (i.e.,observing brittle wood due to a colony of termites).

Therefore, realizing a cognitive system utilizing smart devices for pestcontrol has a fundamental interest in the agriculture and pest controlindustry.

SUMMARY

According to one embodiment of the present invention, a method isprovided. The method comprising: detecting one or more pests based onreceiving sensor data from one or more sensors associated with apredefined location; analyzing the sensor data with cognitive machinelearning based on the detected pests; generating a treatmentrecommendation report based on the analysis; and outputting thetreatment recommendation report.

Another embodiment of the present invention, a computer program productis provided. The computer program product comprising: one or morecomputer readable storage devices and program instructions stored on theone or more computer readable storage devices, the stored programinstructions comprising: program instructions to detect one or morepests based on receiving sensor data from one or more sensors associatedwith a predefined location; program instructions to analyze the sensordata with cognitive machine learning based on the detected pests;program instructions to generate a treatment recommendation report basedon the analysis; and program instructions to output the treatmentrecommendation report.

Another embodiment of the present invention, a computer system isprovided. The computer system comprising: one or more computerprocessors; one or more computer readable storage devices; programinstructions stored on the one or more computer readable storage devicesfor execution by at least one of the one or more computer processors,the stored program instructions comprising: program instructions todetect one or more pests based on receiving sensor data from one or moresensors associated with a predefined location; program instructions toanalyze the sensor data with cognitive machine learning based on thedetected pests; program instructions to generate a treatmentrecommendation report based on the analysis; and program instructions tooutput the treatment recommendation report.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram illustrating the topology of thehost server pest control environment 100, in accordance with anembodiment of the present invention;

FIG. 2 is a functional block diagram illustrating the components of pestcontrol component 111, in accordance with an embodiment of the presentinvention;

FIG. 3 is a flowchart, designated as 300, depicting operational steps ofmethod for executing the host server pest control environment 100, inaccordance with an embodiment of the present invention; and

FIG. 4 depicts a block diagram, designated as 400, of components of theserver computer executing the program within the host server, inaccordance with an embodiment of the present invention.

DETAILED DESCRIPTION

Embodiments of the present invention recognize that improvements to pestcontrol can be made by using machine learning techniques in conjunctionwith smart sensors. The invention leverages IoT (Internet of Things)technology to capture sounds and images to identify various pests in anenvironment (e.g., farms, homes, etc.). After storing the capturedinputs, the system uses machine learning techniques to predict whatactions are required based on historical infestations that have affectedthe location previously or similar locations previously. If theinfestations are new then the system will analyze and recommend anaction based on the current data. The system can account for severalvariables such as, the time of the year, predicted weather conditionsand specific crops. By using this system, a user can take proactiveaction before they would see a problem solely relying on human tracking.

Detailed description of embodiments of the claimed structures andmethods are disclosed herein; however, it is to be understood that thedisclosed embodiments are merely illustrative of the claimed structuresand methods that may be embodied in various forms. In addition, each ofthe examples given in connection with the various embodiments isintended to be illustrative, and not restrictive. Further, the figuresare not necessarily to scale, some features may be exaggerated to showdetails of particular components. Therefore, specific structural andfunctional details disclosed herein are not to be interpreted aslimiting, but merely as a representative basis for teaching one skilledin the art to variously employ the methods and structures of the presentdisclosure.

References in the specification to “one embodiment”, “an embodiment”,“an example embodiment”, etc., indicate that the embodiment describedmay include a particular feature, structure, or characteristic, butevery embodiment may not necessarily include the particular feature,structure, or characteristic. Moreover, such phrases are not necessarilyreferring to the same embodiment. Further, when a particular feature,structure, or characteristic is described in connection with anembodiment, it is submitted that it is within the knowledge of oneskilled in the art to affect such feature, structure, or characteristicin connection with other embodiments, whether or not explicitlydescribed.

FIG. 1 is a functional block diagram illustrating a host server pestcontrol environment, generally designated 100, in accordance with oneembodiment of the present invention. FIG. 1 provides only anillustration of one implementation and does not imply any limitationswith regard to the environments in which different embodiments may beimplemented. Many modifications to the depicted environment may be madeby those skilled in the art without departing from the scope of theinvention as recited by the claims.

Host server pest control environment 100 includes host server 110, audioand visual sensors 120, climate server 130, and reference databaseserver 140, all interconnected over network 103. Network 103 can be, forexample, a telecommunications network, a local area network (LAN), awide area network (WAN), such as the Internet, or a combination of thethree, and can include wired, wireless, or fiber optic connections.Network 103 can include one or more wired and/or wireless networks thatare capable of receiving and transmitting data, voice, and/or videosignals, including multimedia signals that include voice, data, andvideo information. In general, network 103 can be any combination ofconnections and protocols that can support communications between hostserver 110, audio and visual sensors 120, climate server 130, referencedatabase server 140, and other computing devices (not shown) within hostserver pest control environment 100.

Host server 110 can be a standalone computing device, a managementserver, a web server, a mobile computing device, or any other electronicdevice or computing system capable of receiving, sending, and processingdata. In other embodiments, host server 110 can represent a servercomputing system utilizing multiple computers as a server system, suchas in a cloud computing environment. In another embodiment, host server110 can be a laptop computer, a tablet computer, a netbook computer, apersonal computer (PC), a desktop computer, a personal digital assistant(PDA), a smart phone, or any other programmable electronic devicecapable of communicating with audio and visual sensors 120, and othercomputing devices (not shown) within host server pest controlenvironment 100 via network 103. In another embodiment, host server 110represents a computing system utilizing clustered computers andcomponents (e.g., database server computers, application servercomputers, etc.) that act as a single pool of seamless resources whenaccessed within host server pest control environment 100. Host server110 includes pest control component 111 and database 118.

Pest control component 111 enables the present invention to manage andcontrol pest infestations. In the depicted embodiment, pest controlcomponent 111 resides on host server 110. In another embodiment, pestcontrol component 111 can reside on climate server 130, or referencedatabase server 140. In the depicted embodiment, pest control component111 consists of several components (refer to FIG. 2 ) such as pestdetection component 112, target of pest component 113, data gatheringcomponent 114, analysis component 115, and cognitive sub-componentcomponent 116.

Database 118 is a repository for data used by pest control component111. In the depicted embodiment, database 118 resides on host server110. In another embodiment, database 118 may reside elsewhere withinhost server pest control environment provided that pest controlcomponent 111 has access to database 118. A database is an organizedcollection of data. Database 118 can be implemented with any type ofstorage device capable of storing data and configuration files that canbe accessed and utilized by host server 110, such as a database server,a hard disk drive, or a flash memory. Database 118 uses one or more of aplurality of techniques known in the art to store a plurality ofinformation. For example, database 118 may store information such asvegetation information and pest information. In another example,database 118 can contain historical information regarding priortreatment, prior infestations, prior user actions and prior weatherconditions.

Audio and visual sensors 120 are one or more specialized devices (e.g.,IoT wireless camera, IoT wireless microphone, etc.) that are capable ofdetecting and process audio and visual environment in the vicinity ofthe devices. In another embodiment, audio and visual sensors 120 caninclude autonomous drones equipped with onboard camera and microphone toaid in identification of pests.

Climate server 130 contains weather related information, such as, but isnot limited to, a historical weather pattern, a current weathercondition, and a future weather forecast. Climate server 130 can be astandalone computing device, a management server, a web server, a mobilecomputing device, or any other electronic device or computing systemcapable of receiving, sending, and processing data. In otherembodiments, climate server 130 can represent a server computing systemutilizing multiple computers as a server system, such as in a cloudcomputing environment. In another embodiment, host server 110 can be alaptop computer, a tablet computer, a netbook computer, a personalcomputer (PC), a desktop computer, a personal digital assistant (PDA), asmart phone, or any other programmable electronic device capable ofcommunicating with reference database server 140, and other computingdevices (not shown) within host server pest control environment 100 vianetwork 103. In another embodiment, climate server 130 represents acomputing system utilizing clustered computers and components (e.g.,database server computers, application server computers, etc.) that actas a single pool of seamless resources when accessed within host serverpest control environment 100.

Reference database server 140 is a server that contains variousreference related information that may help diagnose and treat pestinfestation. For example, reference database server 140 can climate andweather related information for the location of the users. Referencedatabase server 140 can be a standalone computing device, a managementserver, a web server, a mobile computing device, or any other electronicdevice or computing system capable of receiving, sending, and processingdata. In other embodiments, reference database server 140 can representa server computing system utilizing multiple computers as a serversystem, such as in a cloud computing environment.

FIG. 2 is a functional block diagram illustrating the components of pestcontrol component 111, in accordance with an embodiment of the presentinvention. Pest control component 111 includes pest detection component112, target of pest component 113, data gathering component 114 andanalysis component 115 along with cognitive sub-component 116.

Pest detection component 112 of the present invention provides thecapability of identifying and discerning of pests based on the receiveddata from audio and visual sensors 120 (e.g., IoT wireless camera, IoTwireless microphones, drones, etc.). In an embodiment, pest detectioncomponent 112 can recognize and differentiate various types of pestsnearby the users. For example, pest detection component 112 identifies aswarm of beetles (e.g., Southern Corn Leaf Beatle, Asiatic gardenbeetle, etc.) known to target corn fields by the auditory pattern oftheir wingbeats. In another example, pest detection component 112detects a sound of wood being chewed on an apple tree. However, it isnot able to determine the exact pest type, therefore, pest detectioncomponent 112 may send an autonomous drone to investigate and validatethe type of pests.

Target of pest component 113 of the present invention provides thecapability of identifying and discerning the target of pest infestation(e.g., livestock, vegetation, etc.) based on the received data from thevarious sensors (e.g., IoT wireless camera, IoT wireless microphones,etc.). In an embodiment, target of pest component 113 can recognize anddifferentiate various types of vegetation (i.e. crops) grown by theusers being targeted by pests. For example, a swarm of Southern CornLeaf beetles identified by pest detection component 112 was detectedwithin a vicinity of the corn field belonging to the user. Target ofpest component 113 correctly identifies the target of the beetles, thecorn field via IoT devices.

In another embodiment, target of pest component 113 can recognize anddifferentiate livestock that are targeted by pests. For example, gnatand flies were identified earlier by pest detection component 112.However, little is known what the pests are doing in the vicinity on thefarm. An IoT device detects these pest buzzing around the herd of cattlegrazing on the farm. Therefore, target of pest component 113 is able tocorrectly identify the target, the herd of cattle, of the gnats andflies.

In yet another embodiment, target of pest component 113 can recognizeand differentiate the target of termites (i.e., wood beam of thefoundation of a house). For example, target of pest component 113 canidentify the type of wood being targeted by termites. Target of pestcomponent 113 is able to determine the pine wood frame of the house ofthe user as the target of termites based on the information sent by theIoT devices where the device picked up the sound of termites chewing onthe wood.

Data gathering component 114 of the present invention provides thecapability of retrieving various information (e.g., reference data oninsects, reference data on the weather, etc.) from various databases(e.g., reference database server 140, etc.). In an embodiment, pestdetection component 112 can retrieve vegetation data (i.e., corn) fromreference database server 140. For example, database server 140 cancontain general reference data on the corn crop such as ideal moisturelevel, ideal growing temperature, and ideal soil composition. This willfurther aid pest control component 111 with identifying issues (e.g.,pests, weather, etc.) that might hinder optimal corn production.

In an embodiment, pest detection component 112 can retrieve pest data(i.e., corn beetle) from reference database server 140. For example,reference database server 140 can contain general reference data on thecorn beetle such as favorite crop/source of food, life span, andgestation period and habit/behavior pattern. This will further aid pestcontrol component 111 with identifying issues that might hinder optimalcorn production.

Analysis component 115 of the present invention provides the capabilityof analyzing all received data and determining the several options forpest treatment. It is noted that analysis component 115 contains asubcomponent, cognitive sub-component 116. In an embodiment, pestdetection component 112 can recognize a new pest infestation andimmediately determine one or more options for the user. In anotherembodiment, pest detection component 112 through cognitive sub-component116 can recognize an early sign of pest infestation based on similarsituation (i.e., same crops and pests) from the prior year and make asimilar recommendation for treatment. Additionally, cognitivesub-component 116 can learn (e.g., machine learning, deep learning,etc.) over time of new, current and/or prior infestation and take acorrective action. It is noted that not all recommendation made byanalysis component 115 results in an extermination plan for pests. It ispossible that no action is taken based on pre-determined parameters. Forexample, a crop loss percentage of 10% is programmed into the systemwhen the growth of corn is decimated by pests and/or weather, the pestcontrol system should only take action when the loss is greater thanthat 10% threshold. In another example, no action is taken on a swarm ofbeetles, targeting wheat, due to the incoming weather pattern where acold front is predicted to come through the farm. The cold front is coldenough to kill the beetles but not cold enough to hurt the wheat fields.

FIG. 3 is a flowchart, designated as 300, depicting operational steps ofmethod for executing the host server pest control environment 100, inaccordance with an embodiment of the present invention.

Pest control component 111 detect pest(s) (step 302). In an embodiment,pest control component 111 through pest detection component 112automatically detect the presence of pests based on audio and visualsensors 120 located throughout the land and/or dwelling belonging to theusers. For example, an IoT microphone detects sound of a large number ofearworm larvae emerging from their cocoon. In order to confirm the typeof larvae, pest control component 111 through pest detection component112 can control the nearest video IoT devices to capture the picture ofthe larvae for a visual confirmation and identification. Furthermore,pest control component 111 through pest detection component 112 candirect autonomous devices such as drones to investigate the source ofthe sound and confirm the type of insects by a visual identificationprocess. The visual identification method can leverage any currenttechnology to identify pests.

After detecting pests, pest control component 111 determines the targetof the pests. In an embodiment, pest control component 111 throughtarget of pest component 113 determine the type of crops (e.g.,agriculture crops, a single rose bush, etc.) affected by the identifiedpest from the prior step (step 302). For example, using audio and visualsensors 120 (i.e., smart devices) located throughout the property, pestcontrol component 111 through target of pest component 113 confirms thetype of vegetation (i.e., corn) where the earworm larvae have emerged.

In another embodiment, pest control component 111 through target of pestcomponent 113 determine the type of livestock (e.g., cattle, pigs, etc.)affected by the identified pest from the prior step (step 302). Forexample, using audio and visual sensors 120 (i.e., smart devices)located throughout the property, pest control component 111 throughtarget of pest component 113 confirms the type of livestock (i.e.,cattle) where the gnats and flies have been accumulating.

In yet another embodiment, pest control component 111 through target ofpest component 113 determine the type of livestock (e.g., cattle, pigs,etc.) affected by the identified pest from the prior step (step 302).For example, using smart dev audio and visual sensors 120 (i.e., smartdevices) located throughout the property, pest control component 111through target of pest component 113 confirms the type of livestock(i.e., cattle) where the gnats and flies have been accumulating.

In another embodiment, pest control component 111 does not determinevegetation type since the identified pest is not associated withconsumption of any vegetation

In yet another embodiment, target of pest component 113 can recognizeand differentiate the target of termites (i.e. wood). For example,target of pest component 113 can identify the type of wood beingtargeted by termites. Target of pest component 113 is able to determinethe pine wood frame of the house of the user as the target of termitesbased on the information sent by the audio and visual sensors 120 (i.e.,smart devices) where the device picked up the sound of termites chewingon the wood and drones were sent to validate the location of theinfestation. Furthermore, pest control component 111 can detect andidentify a family of pigeons in the attic of the house of the user.However, there were no property (i.e., attic eve) destroyed since thepigeons were merely using the outside nook of the attic for shelter.

After detecting the pest and target of pests, pest detection component112 can retrieve data to help with the decision making process. In anembodiment, pest control component 111 retrieves reference data to helpperform further analysis on the new (i.e., no prior interaction)identified pest. For example, pest control component 111 retrievesvarious data (e.g., life span, nesting habits, source of food, etc.)from reference database server 140 associated with the family of pigeonsliving in attic.

Pest control component 111 analyzes data (step 304). In an embodiment,pest control component 111 can analyze data through data gatheringcomponent 114. Data gathering component 114 can aggregated data from thefollowing sources, but not limited to, pest detection component 112,target of pest component 113, database 118, climate server 130, andreference database server 140.

Pest control component 111 generate treatment plan (step 306). Afteraggregating data, pest control component 111 can start analyzing thedata via analysis component 115. The analysis process can involveseveral decision selections such as choosing the best type of pestcontrol method (e.g., biological predator, pesticides, physical traps,etc.) and the best application of the pest control method (i.e., humanworker to trap or send in drones to drop pesticides).

In an embodiment, pest control component through cognitive sub-component116 can recall if the detected pests were from similar interactions. Ifcognitive sub-component 116 recalls from database 118 that theinfestations were the same then it can retrieve the treatment plan fromprevious year. For example, pest control component 111 determines thatthe earworm larvae infested the corn crop last year (i.e., spring time)in the same section of the farm from database 118. Otherwise, if thesituation has changed such as the weather is different (i.e., freezingspell) and could possibly interfere with the pests without interactionfrom pest detection component 112 then cognitive sub-component 116 canchoose to ignore the previous treatment plan and create a new one basedon new conditions.

Pest control component outputs treatment plan (step 308). In anembodiment, after retrieving prior interaction data and analyzing othercurrent variables (e.g., weather conditions, soil conditions, etc.) pestcontrol component 111 through analysis component 115 determinestreatment plan and outputs the treatment recommendation report to theuser. Pest control component 111 can wait for user input regarding thenext steps. For example, the user may tell pest control component 111 totake no further action or that the user may take action themselves basedon the treatment recommendation report. It is noted that pest controlcomponent 111 can bypass the user input and perform pest control basedon the extermination plan by using remote autonomous devices (e.g.,drones with pesticides, etc.) if the user has that feature enabled inthe setting. It is further noted that some identified pests may notwarrant an extermination solution. For example, a single bug wasidentified eating a peach. Based on historical data for that crop andsmall volume grown, pest control component 111 determines that notreatment is necessary at the moment and recommends constant monitoring.It is noted that a treatment plan can be created/output to the users foreach infestations. The user can select the frequency on receiving thetreatment recommendation report or can set the system to autonomouslyand automatically exterminate pests without any user input (i.e. zerotreatment plan output). It is further noted that cognitive sub-component116 can save all decisions, analysis and treatment plans in database 118as part of the unsupervised learning process (i.e., cognitive learning).

In another embodiment, pest control component 111 does not output atreatment plan based on the weather forecast. For example, a cold frontis moving into the area that will kill off the pests (i.e. corn beetle)but not the crops (i.e. corn). Therefore, pest control component 111determines no action is needed regarding the corn beetle. However, atreatment recommendation report is generated and sent to the users.

FIG. 4 depicts a block diagram, designated as 400, of components of theserver computer executing the program within the host server acceleratorenvironment of FIG. 1 , in accordance with an embodiment of the presentinvention.

Host server 110 can include processor(s) 404, cache 416, memory 406,persistent storage 408, communications unit 410, input/output (I/O)interface(s) 412 and communications fabric 402. Communications fabric402 provides communications between cache 416, memory 406, persistentstorage 408, communications unit 410, and input/output (I/O)interface(s) 412. Communications fabric 402 can be implemented with anyarchitecture designed for passing data and/or control informationbetween processors (such as microprocessors, communications and networkprocessors, etc.), system memory, peripheral devices, and any otherhardware components within a system. For example, communications fabric402 can be implemented with one or more buses.

Memory 406 and persistent storage 408 are computer readable storagemedia. In this embodiment, memory 406 includes random access memory(RAM). In general, memory 406 can include any suitable volatile ornon-volatile computer readable storage media. Cache 416 is a fast memorythat enhances the performance of processor(s) 404 by holding recentlyaccessed data, and data near recently accessed data, from memory 406.

Program instructions and data used to practice embodiments of thepresent invention, e.g., pest control component 111 and database 118,can be stored in persistent storage 408 for execution and/or access byone or more of the respective processor(s) 404 of host server 110 viamemory 406. In this embodiment, persistent storage 408 includes amagnetic hard disk drive. Alternatively, or in addition to a magnetichard disk drive, persistent storage 408 can include a solid-state harddrive, a semiconductor storage device, a read-only memory (ROM), anerasable programmable read-only memory (EPROM), a flash memory, or anyother computer readable storage media that is capable of storing programinstructions or digital information.

The media used by persistent storage 408 may also be removable. Forexample, a removable hard drive may be used for persistent storage 408.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer readable storage medium that is also part of persistent storage408.

Communications unit 410, in these examples, provides for communicationswith other data processing systems or devices, including resources ofclimate server 130. In these examples, communications unit 410 includesone or more network interface cards. Communications unit 410 may providecommunications through the use of either or both physical and wirelesscommunications links. Pest control component 111 and database 118 may bedownloaded to persistent storage 408 of host server 110 throughcommunications unit 410.

I/O interface(s) 412 allows for input and output of data with otherdevices that may be connected to host server 110. For example, I/Ointerface(s) 412 may provide a connection to external device(s) 418 suchas a keyboard, a keypad, a touch screen, a microphone, a digital camera,and/or some other suitable input device. External device(s) 418 can alsoinclude portable computer readable storage media such as, for example,thumb drives, portable optical or magnetic disks, and memory cards.Software and data used to practice embodiments of the present invention,e.g., pest control component 111 and database 118 on host server 110,can be stored on such portable computer readable storage media and canbe loaded onto persistent storage 408 via I/O interface(s) 412. I/Ointerface(s) 412 also connect to a display 420.

Display 420 provides a mechanism to display data to a user and may be,for example, a computer monitor or the lenses of a head mounted display.Display 420 can also function as a touchscreen, such as a display of atablet computer.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be any tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, a special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, a segment, or aportion of instructions, which comprises one or more executableinstructions for implementing the specified logical function(s). In somealternative implementations, the functions noted in the blocks may occurout of the order noted in the Figures. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The terminology used herein was chosen to best explain the principles ofthe embodiment, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

What is claimed is:
 1. A method for pest control, the method comprising:detecting one or more pests based on receiving sensor data from one ormore sensors associated with a predefined location, wherein detectingthe one or more pests comprising: receiving an auditory pattern based onthe one or more pests by the one or more sensors wherein the auditorypattern comprises sounds made by the one or more pests; determining apest type based on the received auditory pattern; responsive todetermining the pest type, identifying the one or more pests; andresponsive to not able to determine the pest type, verifying the one ormore pests based on visual identification from the one or more sensors;analyzing the sensor data with cognitive machine learning based on thedetected pests; generating a treatment recommendation report based onthe analysis; and outputting the treatment recommendation report.
 2. Themethod of claim 1, wherein the sounds made by the one or more pestsconsisting of wingbeat pattern of certain insects, chewing woods andsound of larvae merging from their cocoon stage.
 3. The method of claim1, wherein analyzing the sensor data with cognitive machine learningbased on the detected pests further comprises: aggregating the one ormore sensor data, a reference data, a first database, and climate data,wherein the first database comprises historical data; selecting a pestcontrol method based on the identified one or more pests, wherein thepest control method comprises at least one of pesticides, traps, andbiological predators; and calculating action plan to apply the pestcontrol method.
 4. The method of claim 3, further comprises: retrievingdata from the first database, wherein the data comprises one or moreprevious treatment plans.
 5. The method of claim 1, wherein outputtingthe treatment recommendation report further comprises: sending thetreatment recommendation report to one or more users and awaiting userresponse; responsive to user deciding to ignore the treatmentrecommendation report, saving the report to a first database; andresponsive to user deciding perform a pest control method manually basedon the treatment recommendation report, saving the report to a firstdatabase.
 6. The method of claim 5, further comprises: responsive touser deciding to allow automatic treatment, sending an autonomous deviceto perform a pest control method based on the treatment recommendationreport.
 7. The method of claim 1, wherein the one or more sensorscomprise at least one of a camera, a microphone, or a drone.
 8. Acomputer program product for pest control, the computer program productcomprising: one or more computer readable storage devices and programinstructions stored on the one or more computer readable storagedevices, the stored program instructions comprising: programinstructions to detect one or more pests based on receiving sensor datafrom one or more sensors associated with a predefined location, whereinprogram instructions to detect the one or more pests comprising: programinstructions to receive an auditory pattern based on the one or morepests by the one or more sensors wherein the auditory pattern comprisessounds made by the one or more pests; program instructions to determinea pest type based on the received auditory pattern; responsive todetermining the pest type, program instructions to identify the one ormore pests; and responsive to not able to determine the pest type,program instructions to verify the one or more pests based on visualidentification from the one or more sensors: program instructions toanalyze the sensor data with cognitive machine learning based on thedetected pests; program instructions to generate a treatmentrecommendation report based on the analysis; and program instructions tooutput the treatment recommendation report.
 9. The computer programproduct of claim 8, wherein the sounds made by the one or more pestsconsisting of wingbeat pattern of certain insects, chewing woods andsound of larvae merging from their cocoon stage.
 10. The computerprogram product of claim 8, wherein program instructions to analyze thesensor data with cognitive machine learning based on the detected pestsfurther comprises: program instructions to aggregate the one or moresensor data, a reference data, a first database, and climate data,wherein the first database comprises historical data; programinstructions to select a pest control method based on the identified oneor more pests, wherein the pest control method comprises at least one ofpesticides, traps, and biological predators; and program instructions tocalculate action plan to apply the pest control method.
 11. The computerprogram product of claim 10, the stored program instructions furthercomprises: program instructions to retrieve data from the firstdatabase, wherein the data comprises one or more previous treatmentplans.
 12. The computer program product of claim 8, wherein programinstructions to output the treatment recommendation report furthercomprises: program instructions to send the treatment recommendationreport to one or more users and awaiting user response; responsive touser deciding to ignore the treatment recommendation report, programinstructions to save the report to a first database; and responsive touser deciding perform a pest control method manually based on thetreatment recommendation report, program instructions to save the reportto a first database.
 13. The computer program product of claim 12, thestored program instructions further comprises: responsive to userdeciding to allow automatic treatment, program instructions to send anautonomous device to perform a pest control method based on thetreatment recommendation report.
 14. The computer program product ofclaim 8, wherein the one or more sensors comprise at least one of acamera, a microphone, or a drone.
 15. A computer system for pestcontrol, the computer system comprising: one or more computerprocessors; one or more computer readable storage devices; programinstructions stored on the one or more computer readable storage devicesfor execution by at least one of the one or more computer processors,the stored program instructions comprising: program instructions todetect one or more pests based on receiving sensor data from one or moresensors associated with a predefined location, wherein programinstructions to detect the one or more pests comprising: programinstructions to receive an auditory pattern based on the one or morepests by the one or more sensors wherein the auditory pattern comprisessounds made by the one or more pests; program instructions to determinea pest type based on the received auditory pattern; responsive todetermining the pest type, program instructions to identify the one ormore pests; and responsive to not able to determine the pest type,program instructions to verify the one or more pests based on visualidentification from the one or more sensors: program instructions toanalyze the sensor data with cognitive machine learning based on thedetected pests; program instructions to generate a treatmentrecommendation report based on the analysis; and program instructions tooutput the treatment recommendation report.
 16. The computer system ofclaim 15, wherein the sounds made by the one or more pests consisting ofwingbeat pattern of certain insects, chewing woods and sound of larvaemerging from their cocoon stage.
 17. The computer system of claim 15,wherein program instructions to analyze the sensor data with cognitivemachine learning based on the detected pests further comprises: programinstructions to aggregate the one or more sensor data, a reference data,a first database, and climate data, wherein the first database compriseshistorical data; program instructions to select a pest control methodbased on the identified one or more pests, wherein the pest controlmethod comprises at least one of pesticides, traps, and biologicalpredators; and program instructions to calculate action plan to applythe pest control method.
 18. The computer system of claim 17, the storedprogram instructions further comprises: program instructions to retrievedata from the first database, wherein the data comprises one or moreprevious treatment plans.
 19. The computer system of claim 15, whereinprogram instructions to output the treatment recommendation reportfurther comprises: program instructions to send the treatmentrecommendation report to one or more users and awaiting user response;responsive to user deciding to ignore the treatment recommendationreport, program instructions to save the report to a first database; andresponsive to user deciding perform a pest control method manually basedon the treatment recommendation report, program instructions to save thereport to a first database.
 20. The computer system of claim 15, thestored program instructions further comprises: responsive to userdeciding to allow automatic treatment, program instructions to send anautonomous device to perform a pest control method based on thetreatment recommendation report.